{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Apple', 'Banana', 'Bean', 'Broccoli', 'Cabbage', 'Capsicum', 'Carambola', 'Carrot', 'Cauliflower', 'Cucumber', 'Guava', 'Kiwi', 'Mango', 'muskmelon', 'Orange', 'Peach', 'Pear', 'Persimmon', 'Pitaya', 'Plum', 'Pomegranate', 'Potato', 'Pumpkin', 'Radish', 'Tomatoe']\n",
      "此模型包含种类: 25 种\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, Activation, Dropout, Lambda\n",
    "from tensorflow.keras.optimizers import Adadelta\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n",
    "\n",
    "\n",
    "# 学习率\n",
    "learning_rate = 0.1  # initial learning rate\n",
    "# 最低的学习率\n",
    "min_learning_rate = 0.00001\n",
    "# 学习率变化倍率\n",
    "learning_rate_reduction_factor = 0.5\n",
    "\n",
    "# 当损失趋于稳定时，在降低学习率之前需要等待多少时间\n",
    "patience = 3\n",
    "# 控制训练和测试期间完成的日志记录量：0-无，1-在每个批次后报告度量，2-在每个时期后报告度量\n",
    "verbose = 1  # 0 - none, 1 - reports metrics after each batch, 2 - reports metrics after each epoch\n",
    "\n",
    "# 图片预处理\n",
    "image_size = (100, 100)  # width and height of the used images\n",
    "input_shape = (100, 100, 3)\n",
    "\n",
    "\n",
    "base_dir = './'\n",
    "test_dir = os.path.join(base_dir, 'Training')\n",
    "train_dir = os.path.join(base_dir, 'Training')\n",
    "output_dir = 'output_files'\n",
    "\n",
    "\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n",
    "\n",
    "labels = os.listdir(train_dir)\n",
    "num_classes = len(labels)\n",
    "\n",
    "# 创建两个图表，一个表示准确率，一个表示损失率，以显示训练过程中这两个指标的演变\n",
    "def plot_model_history(model_history, out_path=\"\"):\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "    print(\"model_history.history\", model_history.history)\n",
    "    \n",
    "    # 设置准确率图表的标题、y轴名、x轴名\n",
    "    axs[0].plot(range(1, len(model_history.history['accuracy']) + 1), model_history.history['accuracy'])\n",
    "    axs[0].set_title('Model Accuracy')\n",
    "    axs[0].set_ylabel('Accuracy')\n",
    "    axs[0].set_xlabel('Epoch')\n",
    "    # 设置刻度\n",
    "    axs[0].set_xticks(np.arange(1, len(model_history.history['accuracy']) + 1))\n",
    "    # 添加图例\n",
    "    axs[0].legend(['train'], loc='best')\n",
    "    \n",
    "    # 损失率图表\n",
    "    axs[1].plot(range(1, len(model_history.history['loss']) + 1), model_history.history['loss'])\n",
    "    axs[1].set_title('Model Loss')\n",
    "    axs[1].set_ylabel('Loss')\n",
    "    axs[1].set_xlabel('Epoch')\n",
    "    axs[1].set_xticks(np.arange(1, len(model_history.history['loss']) + 1))\n",
    "    axs[1].legend(['train'], loc='best')\n",
    "\n",
    "    # 保存图片\n",
    "    plt.savefig(output_dir + \"/acc_loss.png\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 创建混淆矩阵，直观地表示分类错误的图像\n",
    "def plot_confusion_matrix(y_true, y_pred, classes):\n",
    "    print(\"plot_confusion_matrix\", y_true, y_pred, classes)\n",
    "\n",
    "    # 建立混淆矩阵\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    df_cm = pd.DataFrame(cm, index=[i for i in classes], columns=[\n",
    "                         i for i in classes])\n",
    "    plt.figure(figsize=(40, 40))\n",
    "\n",
    "    # 绘制热力图\n",
    "    ax = sn.heatmap(df_cm, annot=True, square=True, fmt=\"d\",\n",
    "                    linewidths=.2, cbar_kws={\"shrink\": 0.8})\n",
    "\n",
    "    # 保存矩阵\n",
    "    plt.savefig(output_dir + \"/confusion_matrix.png\")\n",
    "    plt.show()\n",
    "    return ax\n",
    "\n",
    "\n",
    "# 随机改变图像的色调和饱和度，以模拟可变的照明条件\n",
    "def augment_image(x):\n",
    "    # 随机饱和度\n",
    "    x = tf.image.random_saturation(x, 0.9, 1.2)\n",
    "    # 随机色调\n",
    "    x = tf.image.random_hue(x, 0.02)\n",
    "    return x\n",
    "\n",
    "# 获取训练集和测试集的目录路径，生成两个生成器\n",
    "# 1. 训练生成器使用来自训练集的图像，它应用随机的水平和垂直翻转来增强数据，并随机生成批次\n",
    "# 2. 测试生成器使用来自测试集的图像，不生成随机批次\n",
    "# 每次训练完成，使用测试集计算 accuracy 准确率和 loss 损失率\n",
    "def build_data_generators(train_folder, test_folder, labels=None, image_size=(100, 100), batch_size=50):\n",
    "    train_datagen = ImageDataGenerator(\n",
    "        width_shift_range=0.0,\n",
    "        height_shift_range=0.0,\n",
    "        zoom_range=0.0,\n",
    "        horizontal_flip=True,\n",
    "        vertical_flip=True,  # randomly flip images\n",
    "        preprocessing_function=augment_image)  # percentage indicating how much of the training set should be kept for validation\n",
    "\n",
    "    test_datagen = ImageDataGenerator()\n",
    "    train_gen = train_datagen.flow_from_directory(train_folder, target_size=image_size, class_mode='sparse',\n",
    "                                                  batch_size=batch_size, shuffle=True, subset='training', classes=labels)\n",
    "    test_gen = test_datagen.flow_from_directory(test_folder, target_size=image_size, class_mode='sparse',\n",
    "                                                batch_size=batch_size, shuffle=False, subset=None, classes=labels)\n",
    "    return train_gen, test_gen\n",
    "\n",
    "\n",
    "\n",
    "# this method performs all the steps from data setup, training and testing the model and plotting the results\n",
    "# the model is any trainable model; the input shape and output number of classes is dependant on the dataset used, in this case the input is 100x100 RGB images and the output is a softmax layer with 118 probabilities\n",
    "# the name is used to save the classification report containing the f1 score of the model, the plots showing the loss and accuracy and the confusion matrix\n",
    "# the batch size is used to determine the number of images passed through the network at once, the number of steps per epochs is derived from this as (total number of images in set // batch size) + 1\n",
    "def train_and_evaluate_model(model, name=\"\", epochs=25, batch_size=50, verbose=verbose, useCkpt=False):\n",
    "    # print(\"main\", model.summary())\n",
    "    \n",
    "    model_dir = os.path.join(output_dir, name)\n",
    "\n",
    "    if not os.path.exists(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "\n",
    "    # 是否使用之前的模型继续训练\n",
    "    if useCkpt:\n",
    "        model.load_weights(model_dir)\n",
    "\n",
    "    # 训练集、测试集 预处理\n",
    "    trainGen, testGen  = build_data_generators(train_dir, test_dir, \n",
    "    labels=labels, image_size=image_size, batch_size=batch_size)\n",
    "\n",
    "\n",
    "    # 对学习率进行自适应约束\n",
    "    optimizer = Adadelta(learning_rate=learning_rate)\n",
    "\n",
    "    # 配置模型训练函数(optimizer: 优化器, loss: 损失函数, metrics: 监控指标 )\n",
    "    model.compile(optimizer=optimizer, loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "\n",
    "    # 使用 ReduceLROnPlateau 来更新学习率\n",
    "    learning_rate_reduction = ReduceLROnPlateau(monitor='loss', patience=patience, verbose=verbose,\n",
    "                                                factor=learning_rate_reduction_factor, min_lr=min_learning_rate)\n",
    "\n",
    "    # 在每次训练后保存模型\n",
    "    save_model = ModelCheckpoint(filepath=model_dir, monitor='loss', verbose=verbose,\n",
    "                                 save_best_only=True, save_weights_only=False, mode='min', save_freq='epoch')\n",
    "\n",
    "    # 添加回调函数 learning_rate_reduction、learning_rate_reduction\n",
    "    history = model.fit(trainGen,\n",
    "                        epochs=epochs,\n",
    "                        steps_per_epoch=(trainGen.n // batch_size) + 1,\n",
    "                        verbose=verbose,\n",
    "                        callbacks=[learning_rate_reduction, save_model])\n",
    "\n",
    "    # 读取权重\n",
    "    model.load_weights(model_dir)\n",
    "\n",
    "    # 重置训练集\n",
    "    trainGen.reset()\n",
    "\n",
    "    # 预测结果，获取损失率 和 准确率\n",
    "    loss_t, accuracy_t = model.evaluate(trainGen, steps=(\n",
    "        trainGen.n // batch_size) + 1, verbose=verbose)\n",
    "    loss, accuracy = model.evaluate(testGen, steps=(\n",
    "        testGen.n // batch_size) + 1, verbose=verbose)\n",
    "\n",
    "    print(\"Train: accuracy = %f  ;  loss_v = %f\" % (accuracy_t, loss_t))\n",
    "    print(\"Test: accuracy = %f  ;  loss_v = %f\" % (accuracy, loss))\n",
    "\n",
    "    # 绘制结果\n",
    "    plot_model_history(history)\n",
    "\n",
    "    testGen.reset()\n",
    "\n",
    "    # 预测结果\n",
    "    y_pred = model.predict(testGen, steps=(\n",
    "        testGen.n // batch_size) + 1, verbose=verbose)\n",
    "    # 真实结果\n",
    "    y_true = testGen.classes[testGen.index_array]\n",
    "    # 混淆矩阵\n",
    "    plot_confusion_matrix(y_true, y_pred.argmax(axis=-1), labels)\n",
    "\n",
    "    # 输出模型评估报告\n",
    "    class_report = classification_report(\n",
    "        y_true, y_pred.argmax(axis=-1), target_names=labels)\n",
    "    with open(output_dir + \"/classification_report.txt\", \"w\") as text_file:\n",
    "        text_file.write(\"%s\" % class_report)\n",
    "\n",
    "\n",
    "\n",
    "print(labels)\n",
    "print(f\"此模型包含种类: {num_classes} 种\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 53605 images belonging to 25 classes.\n",
      "Found 53605 images belonging to 25 classes.\n",
      "Epoch 1/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 1.9480 - accuracy: 0.4284\n",
      "Epoch 1: loss improved from inf to 1.94796, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 692s 644ms/step - loss: 1.9480 - accuracy: 0.4284 - lr: 0.1000\n",
      "Epoch 2/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.7969 - accuracy: 0.7427\n",
      "Epoch 2: loss improved from 1.94796 to 0.79693, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 388s 362ms/step - loss: 0.7969 - accuracy: 0.7427 - lr: 0.1000\n",
      "Epoch 3/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.5244 - accuracy: 0.8270\n",
      "Epoch 3: loss improved from 0.79693 to 0.52440, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 962s 897ms/step - loss: 0.5244 - accuracy: 0.8270 - lr: 0.1000\n",
      "Epoch 4/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.3947 - accuracy: 0.8673\n",
      "Epoch 4: loss improved from 0.52440 to 0.39473, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 381s 355ms/step - loss: 0.3947 - accuracy: 0.8673 - lr: 0.1000\n",
      "Epoch 5/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.3140 - accuracy: 0.8939\n",
      "Epoch 5: loss improved from 0.39473 to 0.31399, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 393s 366ms/step - loss: 0.3140 - accuracy: 0.8939 - lr: 0.1000\n",
      "Epoch 6/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.2564 - accuracy: 0.9134\n",
      "Epoch 6: loss improved from 0.31399 to 0.25641, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 396s 369ms/step - loss: 0.2564 - accuracy: 0.9134 - lr: 0.1000\n",
      "Epoch 7/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.2231 - accuracy: 0.9248\n",
      "Epoch 7: loss improved from 0.25641 to 0.22310, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 396s 369ms/step - loss: 0.2231 - accuracy: 0.9248 - lr: 0.1000\n",
      "Epoch 8/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.1876 - accuracy: 0.9359\n",
      "Epoch 8: loss improved from 0.22310 to 0.18759, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 392s 365ms/step - loss: 0.1876 - accuracy: 0.9359 - lr: 0.1000\n",
      "Epoch 9/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.1662 - accuracy: 0.9431\n",
      "Epoch 9: loss improved from 0.18759 to 0.16615, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 403s 376ms/step - loss: 0.1662 - accuracy: 0.9431 - lr: 0.1000\n",
      "Epoch 10/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.1473 - accuracy: 0.9491\n",
      "Epoch 10: loss improved from 0.16615 to 0.14733, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 397s 370ms/step - loss: 0.1473 - accuracy: 0.9491 - lr: 0.1000\n",
      "Epoch 11/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.1335 - accuracy: 0.9555\n",
      "Epoch 11: loss improved from 0.14733 to 0.13354, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 392s 365ms/step - loss: 0.1335 - accuracy: 0.9555 - lr: 0.1000\n",
      "Epoch 12/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.1193 - accuracy: 0.9588\n",
      "Epoch 12: loss improved from 0.13354 to 0.11930, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 395s 368ms/step - loss: 0.1193 - accuracy: 0.9588 - lr: 0.1000\n",
      "Epoch 13/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.1079 - accuracy: 0.9649\n",
      "Epoch 13: loss improved from 0.11930 to 0.10790, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 389s 363ms/step - loss: 0.1079 - accuracy: 0.9649 - lr: 0.1000\n",
      "Epoch 14/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0969 - accuracy: 0.9682\n",
      "Epoch 14: loss improved from 0.10790 to 0.09693, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 386s 359ms/step - loss: 0.0969 - accuracy: 0.9682 - lr: 0.1000\n",
      "Epoch 15/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0860 - accuracy: 0.9714\n",
      "Epoch 15: loss improved from 0.09693 to 0.08595, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 391s 364ms/step - loss: 0.0860 - accuracy: 0.9714 - lr: 0.1000\n",
      "Epoch 16/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0811 - accuracy: 0.9727\n",
      "Epoch 16: loss improved from 0.08595 to 0.08107, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 392s 365ms/step - loss: 0.0811 - accuracy: 0.9727 - lr: 0.1000\n",
      "Epoch 17/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0734 - accuracy: 0.9752\n",
      "Epoch 17: loss improved from 0.08107 to 0.07338, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 396s 369ms/step - loss: 0.0734 - accuracy: 0.9752 - lr: 0.1000\n",
      "Epoch 18/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0700 - accuracy: 0.9766\n",
      "Epoch 18: loss improved from 0.07338 to 0.07000, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 392s 365ms/step - loss: 0.0700 - accuracy: 0.9766 - lr: 0.1000\n",
      "Epoch 19/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0635 - accuracy: 0.9793\n",
      "Epoch 19: loss improved from 0.07000 to 0.06345, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 406s 378ms/step - loss: 0.0635 - accuracy: 0.9793 - lr: 0.1000\n",
      "Epoch 20/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0586 - accuracy: 0.9806\n",
      "Epoch 20: loss improved from 0.06345 to 0.05864, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 516s 481ms/step - loss: 0.0586 - accuracy: 0.9806 - lr: 0.1000\n",
      "Epoch 21/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0585 - accuracy: 0.9806\n",
      "Epoch 21: loss improved from 0.05864 to 0.05849, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 472s 439ms/step - loss: 0.0585 - accuracy: 0.9806 - lr: 0.1000\n",
      "Epoch 22/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0549 - accuracy: 0.9819\n",
      "Epoch 22: loss improved from 0.05849 to 0.05492, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 408s 380ms/step - loss: 0.0549 - accuracy: 0.9819 - lr: 0.1000\n",
      "Epoch 23/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0531 - accuracy: 0.9831\n",
      "Epoch 23: loss improved from 0.05492 to 0.05309, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 390s 363ms/step - loss: 0.0531 - accuracy: 0.9831 - lr: 0.1000\n",
      "Epoch 24/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0498 - accuracy: 0.9841\n",
      "Epoch 24: loss improved from 0.05309 to 0.04983, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 410s 382ms/step - loss: 0.0498 - accuracy: 0.9841 - lr: 0.1000\n",
      "Epoch 25/25\n",
      "1073/1073 [==============================] - ETA: 0s - loss: 0.0437 - accuracy: 0.9857\n",
      "Epoch 25: loss improved from 0.04983 to 0.04371, saving model to output_files\\test.h5\n",
      "1073/1073 [==============================] - 417s 388ms/step - loss: 0.0437 - accuracy: 0.9857 - lr: 0.1000\n",
      "1073/1073 [==============================] - 240s 224ms/step - loss: 0.0176 - accuracy: 0.9947\n",
      "1073/1073 [==============================] - 177s 165ms/step - loss: 0.0112 - accuracy: 0.9966\n",
      "Train: accuracy = 0.994721  ;  loss_v = 0.017554\n",
      "Test: accuracy = 0.996623  ;  loss_v = 0.011169\n",
      "model_history.history {'loss': [1.9479625225067139, 0.7969343662261963, 0.5244008302688599, 0.3947254419326782, 0.3139888346195221, 0.2564138174057007, 0.22309960424900055, 0.18759246170520782, 0.16615484654903412, 0.14732560515403748, 0.13354477286338806, 0.11929593235254288, 0.10789868235588074, 0.09692586213350296, 0.08595370501279831, 0.08107340335845947, 0.07338428497314453, 0.06999700516462326, 0.06345203518867493, 0.05863520875573158, 0.05849195271730423, 0.05491533502936363, 0.05308567360043526, 0.04982776939868927, 0.043712932616472244], 'accuracy': [0.4283555746078491, 0.7427479028701782, 0.8269564509391785, 0.8672884702682495, 0.8938718438148499, 0.9134035706520081, 0.9248204231262207, 0.9358642101287842, 0.9431396126747131, 0.9491091966629028, 0.9554519057273865, 0.9588471055030823, 0.9648540019989014, 0.9681746363639832, 0.9713646173477173, 0.9726890921592712, 0.9751702547073364, 0.9765506982803345, 0.97933030128479, 0.9805614948272705, 0.9805988073348999, 0.9819233417510986, 0.9830612540245056, 0.9841432571411133, 0.985728919506073], 'lr': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]}\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1073/1073 [==============================] - 159s 148ms/step\n",
      "plot_confusion_matrix [ 0  0  0 ... 24 24 24] [ 0  0  0 ... 24 24 24] ['Apple', 'Banana', 'Bean', 'Broccoli', 'Cabbage', 'Capsicum', 'Carambola', 'Carrot', 'Cauliflower', 'Cucumber', 'Guava', 'Kiwi', 'Mango', 'muskmelon', 'Orange', 'Peach', 'Pear', 'Persimmon', 'Pitaya', 'Plum', 'Pomegranate', 'Potato', 'Pumpkin', 'Radish', 'Tomatoe']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4000x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 创建一个自定义层，将原始图像从RGB转换为 HSV和 灰度，并连接结果\n",
    "# forming in input of size 100 x 100 x 4\n",
    "def convert_to_hsv_and_grayscale(x):\n",
    "    hsv = tf.image.rgb_to_hsv(x)\n",
    "    gray = tf.image.rgb_to_grayscale(x)\n",
    "    rez = tf.concat([hsv, gray], axis=-1)\n",
    "    return rez\n",
    "\n",
    "\n",
    "# 定义卷积神经网络模型\n",
    "def network(input_shape, num_classes):\n",
    "    img_input = Input(shape=input_shape, name='data')\n",
    "    x = Lambda(convert_to_hsv_and_grayscale)(img_input)\n",
    "    # 卷积 ConV2D(卷积核数, 卷积核大小, 步长)\n",
    "    x = Conv2D(16, (5, 5), strides=(1, 1), padding='same', name='conv1')(x)\n",
    "    # 激活函数\n",
    "    x = Activation('relu', name='conv1_relu')(x)\n",
    "    # 池化 MaxPooling2D(池化窗口大小, 池化步长)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), padding='valid', name='pool1')(x)\n",
    "    x = Conv2D(32, (5, 5), strides=(1, 1), padding='same', name='conv2')(x)\n",
    "    x = Activation('relu', name='conv2_relu')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), padding='valid', name='pool2')(x)\n",
    "    x = Conv2D(64, (5, 5), strides=(1, 1), padding='same', name='conv3')(x)\n",
    "    x = Activation('relu', name='conv3_relu')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), padding='valid', name='pool3')(x)\n",
    "    x = Conv2D(128, (5, 5), strides=(1, 1), padding='same', name='conv4')(x)\n",
    "    x = Activation('relu', name='conv4_relu')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), padding='valid', name='pool4')(x)\n",
    "    # 扁平化\n",
    "    x = Flatten()(x)\n",
    "    # 全连接 Dense(输出空间的维数, 激活函数)\n",
    "    x = Dense(1024, activation='relu', name='fcl1')(x)\n",
    "    # 随机丢弃\n",
    "    x = Dropout(0.2)(x)\n",
    "    x = Dense(256, activation='relu', name='fcl2')(x)\n",
    "    x = Dropout(0.2)(x)\n",
    "    # softmax 分类\n",
    "    out = Dense(num_classes, activation='softmax', name='predictions')(x)\n",
    "    # 生成模型\n",
    "    rez = Model(inputs=img_input, outputs=out)\n",
    "    return rez\n",
    "\n",
    "\n",
    "model = network(input_shape=input_shape, num_classes=num_classes)\n",
    "train_and_evaluate_model(model, name=\"test.h5\", epochs=25)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Fruits-360 CNN.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  },
  "vscode": {
   "interpreter": {
    "hash": "3196968d684371006099b3d55edeef8ed90365227a30deaef86e5d4aa8519be0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
